A few things engineers can learn from designers

Big data is great for feeding ML algorithms, but you quickly face a bandwidth issue when interfacing with humans. The brain is a fantastic information-processing machine and has an unparalleled, innate ability to detect patterns. Sbastien Pierre explains what designers can teach engineers about creating new ways to make large volumes of data understandable at the human level.

Feeding statistical models and ML algorithms are two of the most common uses for big data, but there is a whole range of visually-driven possibilities that engineers may not have fully considered. Data visualization has been successfully used to explore large datasets and communicate findings, taking advantage of the brain’s incredible visual processing power to unlock new information. Over the last few years, designers, artists, and researchers have been exploring in earnest the complex and intriguing aesthetics of data. Engineers can draw on their findings to learn how to tap into large databases and gain insight either by factoring in visual exploration in data-driven tools or improving the way information is presented to the user. Using both theoretical and practical examples, Sébastien Pierre explores user interface and information-architecture patterns that participants can reuse in a variety of contexts from data-driven applications to dashboards, while learning how designers approach these problems and how to translate and apply this perspective in a software-engineering context. Topics include: